minbpe — Minimal and Clean Byte Pair Encoding (BPE) Algorithm for LLM Tokenization

Overview

minbpe is a minimalistic and clean Python implementation of the Byte Pair Encoding (BPE) algorithm, widely used in Large Language Models (LLMs) for tokenization. It offers a straightforward approach to BPE, with thoroughly commented code and a focus on clarity and efficiency. The project stands out for its simplicity and ease of use, making it an excellent choice for developers looking to integrate BPE into their applications without unnecessary complexity.

Key Features

Implements two Tokenizers: BasicTokenizer and RegexTokenizer, each serving different tokenization needs.
Provides functionality for training tokenizer vocabulary and merges, encoding from text to tokens, and decoding from tokens to text.
Includes a GPT4Tokenizer that reproduces the tokenization of GPT-4 from the tiktoken library.
Offers a simple script for training tokenizers and saving vocabularies to disk.

Use Cases

Developers working on NLP applications can use minbpe for efficient tokenization of text data.
Researchers in the field of LLMs can leverage minbpe for training tokenizers and experimenting with different BPE configurations.
Educational purposes, where understanding the inner workings of BPE algorithms is crucial.

Advantages

Concise and well-documented codebase, making it easy to understand and modify.
Supports both basic and regex-based tokenization, catering to a variety of use cases.
Directly compatible with GPT-4 tokenization, allowing for seamless integration with existing tools and frameworks.

Limitations / Considerations

The project is focused on BPE and does not include other tokenization methods, which might be a limitation for applications requiring diverse tokenization strategies.
The implementation is designed for UTF-8 encoded strings, which may not be suitable for all character encodings.

Hugging Face's tokenizers: A fast and efficient tokenizer library for Python, supporting a wide range of tokenization algorithms, including BPE. It differs from minbpe in its broader scope and support for multiple languages and frameworks.
GPT-2 Tokenizer: The original tokenizer implementation from OpenAI's GPT-2 model. It is more complex and tightly integrated with the GPT-2 model, whereas minbpe provides a standalone, simplified BPE implementation.

Basic Information

GitHub: https://github.com/karpathy/minbpe
Stars: 9,978
License: Unknown
Last Commit: 2025-10-03

📊 Project Information

Project Name: minbpe
GitHub URL: https://github.com/karpathy/minbpe
Programming Language: Python
⭐ Stars: 9,978
🍴 Forks: 949
📅 Created: 2024-02-16
🔄 Last Updated: 2025-10-03

🏷️ Project Topics

Topics: [, ]

📚 Documentation

Wikipedia article on BPE

This article is automatically generated by AI based on GitHub project information and README content analysis

minbpe

Project Description